Modeling and Querying Moving Objects with Social Relationships

Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

[1]  Lei Zhao,et al.  Electronic RFID-Based Indoor Moving Objects: Modeling and Applications , 2012 .

[2]  Krzysztof Janowicz,et al.  A Geo-ontology Design Pattern for Semantic Trajectories , 2013, COSIT.

[3]  Ralf Hartmut Güting,et al.  BerlinMOD: a benchmark for moving object databases , 2009, The VLDB Journal.

[4]  Yannis Theodoridis,et al.  On the Generation of Spatiotemporal Datasets , 1999 .

[5]  Ralf Hartmut Güting,et al.  Parallel SECONDO: scalable query processing in the cloud for non-standard applications , 2015, SIGSPACIAL.

[6]  Ralf Hartmut Güting,et al.  A data model and data structures for moving objects databases , 2000, SIGMOD '00.

[7]  Vania Bogorny,et al.  CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects , 2014, Trans. GIS.

[8]  Thomas Brinkhoff,et al.  A Framework for Generating Network-Based Moving Objects , 2002, GeoInformatica.

[9]  Vania Bogorny,et al.  Weka-STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization , 2011, Trans. GIS.

[10]  Shih-Chia Huang,et al.  A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection , 2015, ACM Trans. Intell. Syst. Technol..

[11]  Ralf Hartmut Güting,et al.  Moving Objects beyond Raw and Semantic Trajectories , 2013, IMMoA.

[12]  Stefano Spaccapietra,et al.  Trajectory Ontologies and Queries , 2008 .

[13]  Bo Xu,et al.  Moving objects databases: issues and solutions , 1998, Proceedings. Tenth International Conference on Scientific and Statistical Database Management (Cat. No.98TB100243).

[14]  Zhiming Ding,et al.  UTR-Tree: An Index Structure for the Full Uncertain Trajectories of Network-Constrained Moving Objects , 2008, The Ninth International Conference on Mobile Data Management (mdm 2008).

[15]  Christian S. Jensen,et al.  GPU-Based Computing of Repeated Range Queries over Moving Objects , 2014, 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[16]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[17]  Roberto Tamassia,et al.  Ranking continuous nearest neighbors for uncertain trajectories , 2011, The VLDB Journal.

[18]  Stefano Spaccapietra,et al.  SeMiTri: a framework for semantic annotation of heterogeneous trajectories , 2011, EDBT/ICDT '11.

[19]  Raymond Chi-Wing Wong,et al.  A highly optimized algorithm for continuous intersection join queries over moving objects , 2011, The VLDB Journal.

[20]  Ralf Hartmut Güting,et al.  SECONDO: A Platform for Moving Objects Database Research and for Publishing and Integrating Research Implementations , 2010, IEEE Data Eng. Bull..

[21]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[22]  Shih-Chia Huang,et al.  Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes , 2014, IEEE Transactions on Cybernetics.

[23]  Jie Tang,et al.  Learning to Infer Social Ties in Large Networks , 2011, ECML/PKDD.

[24]  Qingquan Li,et al.  Spatiotemporal data model for network time geographic analysis in the era of big data , 2016, Int. J. Geogr. Inf. Sci..

[25]  Mohamed F. Mokbel,et al.  Mobility and Social Networking: A Data Management Perspective , 2013, Proc. VLDB Endow..

[26]  A. Prasad Sistla,et al.  Modeling and querying moving objects , 1997, Proceedings 13th International Conference on Data Engineering.

[27]  Herve Martin,et al.  An Ontology-Based Approach to Represent Trajectory Characteristics , 2014, 2014 Fifth International Conference on Computing for Geospatial Research and Application.

[28]  Shih-Chia Huang,et al.  Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems , 2015, Inf. Sci..

[29]  Ouri Wolfson,et al.  Cost and imprecision in modeling the position of moving objects , 1998, Proceedings 14th International Conference on Data Engineering.

[30]  Ralf Hartmut Güting,et al.  SECONDO: an extensible DBMS platform for research prototyping and teaching , 2005, 21st International Conference on Data Engineering (ICDE'05).

[31]  Ralf Hartmut Güting,et al.  GMOBench: a benchmark for generic moving objects , 2012, SIGSPATIAL/GIS.

[32]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

[33]  Nikos Mamoulis,et al.  Density-based place clustering in geo-social networks , 2014, SIGMOD Conference.

[34]  Jiyeong Lee,et al.  Development of Indoor Spatial Data Model Using CityGML ADE , 2013 .

[35]  Markus Schneider,et al.  A foundation for representing and querying moving objects , 2000, TODS.

[36]  Markus Schneider Moving Objects in Databases and GIS: State-of-the-Art and Open Problems , 2009 .

[37]  M. Egenhofer,et al.  Point-Set Topological Spatial Relations , 2001 .

[38]  Hye-Young Kang,et al.  A Study on the Development of Indoor Spatial Data Model Using CityGML ADE , 2013 .

[39]  Stefano Spaccapietra,et al.  Semantic trajectories modeling and analysis , 2013, CSUR.

[40]  Konstantinos Kalpakis,et al.  Modeling Moving Objects for Location Based Services , 2001, Infrastructure for Mobile and Wireless Systems.

[41]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[42]  Ralf Hartmut Güting,et al.  Modeling and querying moving objects in networks , 2006, The VLDB Journal.

[43]  Xiaofeng Meng,et al.  DSTTMOD: A Future Trajectory Based Moving Objects Database , 2003, DEXA.

[44]  Dan Cosley,et al.  Inferring social ties from geographic coincidences , 2010, Proceedings of the National Academy of Sciences.

[45]  Xia Wang,et al.  Actively learning to infer social ties , 2012, Data Mining and Knowledge Discovery.

[46]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[47]  Vania Bogorny,et al.  A Conceptual Data Model for Trajectory Data Mining , 2010, GIScience.

[48]  Yu Zheng,et al.  The TM-RTree: an index on generic moving objects for range queries , 2014, GeoInformatica.

[49]  Davy Janssens,et al.  On the Management and Analysis of Our LifeSteps , 2014, SKDD.

[50]  HaRim Jung,et al.  QR-tree: An efficient and scalable method for evaluation of continuous range queries , 2014, Inf. Sci..

[51]  Shih-Chia Huang,et al.  Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[52]  Henda Hajjami Ben Ghézala,et al.  A Semantic Approach for the Modeling of Trajectories in Space and Time , 2009, ER Workshops.

[53]  Shih-Chia Huang,et al.  A background model re-initialization method based on sudden luminance change detection , 2015, Eng. Appl. Artif. Intell..

[54]  Hua Lu,et al.  Indoor - A New Data Management Frontier , 2010, IEEE Data Eng. Bull..

[55]  Dieter Pfoser,et al.  Novel Approaches to the Indexing of Moving Object Trajectories , 2000, VLDB.

[56]  David Taniar,et al.  Enhanced range search with objects outside query range , 2015, World Wide Web.

[57]  José Moreira,et al.  Oporto: A Realistic Scenario Generator for Moving Objects , 1999, Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99.

[58]  René Peinl,et al.  Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j , 2013, EDBT '13.

[59]  Farshad Hakimpour,et al.  A Spatial Data Model for Moving Object Databases , 2014, ArXiv.

[60]  Stuart M. Allen,et al.  Personality and location-based social networks , 2015, Comput. Hum. Behav..

[61]  Stefano Spaccapietra,et al.  Semantic trajectories: Mobility data computation and annotation , 2013, TIST.

[62]  Dimitris Sacharidis,et al.  Continuous monitoring of nearest trajectories , 2014, SIGSPATIAL/GIS.

[63]  Trisalyn A. Nelson,et al.  A review of quantitative methods for movement data , 2013, Int. J. Geogr. Inf. Sci..

[64]  Dimitris Papadias,et al.  Geo-Social Keyword Search , 2015, SSTD.

[65]  Cyrus Shahabi,et al.  Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases , 2004, VLDB.

[66]  Ahmed Lbath,et al.  Moving Object Trajectories Meta-Model And Spatio-Temporal Queries , 2012, ArXiv.

[67]  Henda Hajjami Ben Ghézala,et al.  A Semantic-Based Data Model for the Manipulation of Trajectories: Application to Urban Transportation , 2015, W2GIS.